Atrial Fibrillation (AFIB) is a common sustained rhythm disturbance. Its prevalence is increasing along with age. In prospective studies, the incidence of AFIB increased from less than 0.1% per year in people under 40 to greater than 1.5% per year in women over 80 and greater than 2% in men over 80 years of age. The rate of ischemic stroke among patients with AFIB is between 2 to 7 times that of people without AFIB.
AFIB is a common arrhythmia in patients who have undergone cardiac surgery. It is estimated that almost 1 in 5 patients admitted to intensive care unit will develop atrial fibrillation. Experts from American Heart Association's Council recommend continuous monitoring for patients at high risk for developing postoperative atrial fibrillation until hospital discharge. Therefore there is a need for a reliable AFIB detection system in ECG monitoring devices.
Atrial fibrillation (AFIB) is a supraventricular tachyarrhythmia characterized by uncoordinated atrial activation with consequent deterioration of atrial mechanical function. On the surface electrocardiogram, AFIB is described by the replacement of consistent P waves by rapid oscillations or fibrillatory waves that vary in size, shape, and timing, resulting in an irregular, frequently rapid ventricular response. The irregularity of the heart rate and absence of the normal P wave are important features in detecting AFIB from the ECG signals. However heart rate irregularity exists in many other rhythms such as AV blocks or due to atrial premature beats (APB).
Known methods of AFIB detection are typically based only on RR interval irregularity involving determining an irregularity measure by simple methods like variance of selected RR intervals or more sophisticated methods such as a Markov model, Neural Network and Hidden Markov Model. Simple measures typically attempt to capture and quantify the randomness of RR intervals while modeling approaches try to construct a model for RR irregularity. Given the chaotic nature of AFIB it is unlikely to model the exact behavior of RR irregularity during AFIB. However the models are usually helpful in distinguishing RR irregularity of AFIB from those caused by other cardiac arrhythmias which is the biggest challenge of AFIB detection. Known systems exhibit excessive false positive detection of AFIB e.g. caused by analysis of non-AFIB rhythms. Known systems also exhibit low sensitivity in detection of short episodes of AFIB. A system according to invention principles addresses deficiencies of known systems to improve cardiac condition detection.